

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch



class        lowercase		:



 '''simple docstring'''


 def __init__(self			,			__a			,			__a=14			,			__a=7			,			__a=True			,			__a=True			,			__a=False			,			__a=True			,			__a=99			,			__a=32			,			__a=4			,			__a=4			,			__a=4			,			__a=37			,			__a="gelu"			,			__a=0.1			,			__a=0.1			,			__a=512			,			__a=0.02			,			)					->   List[Any]:






  """simple docstring"""





  UpperCAmelCase__     							=							parent
  UpperCAmelCase__     							=							batch_size
  UpperCAmelCase__     							=							seq_length
  UpperCAmelCase__     							=							is_training
  UpperCAmelCase__     							=							use_input_mask
  UpperCAmelCase__     							=							use_token_type_ids
  UpperCAmelCase__     							=							use_labels
  UpperCAmelCase__     							=							vocab_size
  UpperCAmelCase__     							=							hidden_size
  UpperCAmelCase__     							=							rotary_dim
  UpperCAmelCase__     							=							num_hidden_layers
  UpperCAmelCase__     							=							num_attention_heads
  UpperCAmelCase__     							=							intermediate_size
  UpperCAmelCase__     							=							hidden_act
  UpperCAmelCase__     							=							hidden_dropout_prob
  UpperCAmelCase__     							=							attention_probs_dropout_prob
  UpperCAmelCase__     							=							max_position_embeddings
  UpperCAmelCase__     							=							initializer_range
  UpperCAmelCase__     							=							None
  UpperCAmelCase__     							=							vocab_size - 1
  UpperCAmelCase__     							=							vocab_size - 1
  UpperCAmelCase__     							=							vocab_size - 1




 def     UpperCamelCase__				(self					)					->   Tuple:






  """simple docstring"""





  UpperCAmelCase__     							=							ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size					)

  UpperCAmelCase__     							=							None
  if self.use_input_mask:
   UpperCAmelCase__     							=							random_attention_mask([self.batch_size, self.seq_length]					)

  UpperCAmelCase__     							=							GPTJConfig(
      vocab_size=self.vocab_size			,			n_embd=self.hidden_size			,			n_layer=self.num_hidden_layers			,			n_head=self.num_attention_heads			,			n_positions=self.max_position_embeddings			,			use_cache=UpperCamelCase__			,			bos_token_id=self.bos_token_id			,			eos_token_id=self.eos_token_id			,			pad_token_id=self.pad_token_id			,			rotary_dim=self.rotary_dim			,			)

  return (config, input_ids, input_mask)




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





  UpperCAmelCase__     							=							self.prepare_config_and_inputs()
  UpperCAmelCase__							,       UpperCAmelCase__							,       UpperCAmelCase__     							=							config_and_inputs
  UpperCAmelCase__     							=							{'input_ids': input_ids, 'attention_mask': attention_mask}
  return config, inputs_dict




 def     UpperCamelCase__				(self			,			__a			,			__a			,			__a			,			__a					)					->   int:






  """simple docstring"""





  UpperCAmelCase__     							=							20
  UpperCAmelCase__     							=							model_class_name(UpperCamelCase__					)

  UpperCAmelCase__     							=							model.init_cache(input_ids.shape[0]			,			UpperCamelCase__					)
  UpperCAmelCase__     							=							jnp.ones((input_ids.shape[0], max_decoder_length)			,			dtype='i4'					)

  UpperCAmelCase__     							=							jnp.broadcast_to(
      jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,			(input_ids.shape[0], input_ids.shape[-1] - 1)					)
  UpperCAmelCase__     							=							model(
      input_ids[:, :-1]			,			attention_mask=UpperCamelCase__			,			past_key_values=UpperCamelCase__			,			position_ids=UpperCamelCase__			,			)

  UpperCAmelCase__     							=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,			dtype='i4'					)
  UpperCAmelCase__     							=							model(
      input_ids[:, -1:]			,			attention_mask=UpperCamelCase__			,			past_key_values=outputs_cache.past_key_values			,			position_ids=UpperCamelCase__			,			)

  UpperCAmelCase__     							=							model(UpperCamelCase__					)

  UpperCAmelCase__     							=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
  self.parent.assertTrue(diff < 1E-3			,			msg=F"Max diff is {diff}"					)




 def     UpperCamelCase__				(self			,			__a			,			__a			,			__a			,			__a					)					->   int:






  """simple docstring"""





  UpperCAmelCase__     							=							20
  UpperCAmelCase__     							=							model_class_name(UpperCamelCase__					)

  UpperCAmelCase__     							=							jnp.concatenate(
      [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)]			,			axis=-1			,			)

  UpperCAmelCase__     							=							model.init_cache(input_ids.shape[0]			,			UpperCamelCase__					)
  UpperCAmelCase__     							=							jnp.broadcast_to(
      jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,			(input_ids.shape[0], input_ids.shape[-1] - 1)					)

  UpperCAmelCase__     							=							model(
      input_ids[:, :-1]			,			attention_mask=UpperCamelCase__			,			past_key_values=UpperCamelCase__			,			position_ids=UpperCamelCase__			,			)
  UpperCAmelCase__     							=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,			dtype='i4'					)
  UpperCAmelCase__     							=							model(
      input_ids[:, -1:]			,			past_key_values=outputs_cache.past_key_values			,			attention_mask=UpperCamelCase__			,			position_ids=UpperCamelCase__			,			)

  UpperCAmelCase__     							=							model(UpperCamelCase__			,			attention_mask=UpperCamelCase__					)

  UpperCAmelCase__     							=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
  self.parent.assertTrue(diff < 1E-3			,			msg=F"Max diff is {diff}"					)







@require_flax
class        lowercase		(			__A					,							__A					,							unittest.TestCase   ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE   				=							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 __SCREAMING_SNAKE_CASE   				=							(FlaxGPTJForCausalLM,) if is_flax_available() else ()




 def     UpperCamelCase__				(self					)					->   Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__     							=							FlaxGPTJModelTester(self					)




 def     UpperCamelCase__				(self					)					->   Optional[Any]:






  """simple docstring"""





  for model_class_name in self.all_model_classes:
   UpperCAmelCase__							,       UpperCAmelCase__							,       UpperCAmelCase__     							=							self.model_tester.prepare_config_and_inputs()
   self.model_tester.check_use_cache_forward(UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__					)




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





  for model_class_name in self.all_model_classes:
   UpperCAmelCase__							,       UpperCAmelCase__							,       UpperCAmelCase__     							=							self.model_tester.prepare_config_and_inputs()
   self.model_tester.check_use_cache_forward_with_attn_mask(
       UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__					)




 @tooslow
 def     UpperCamelCase__				(self					)					->   Optional[int]:






  """simple docstring"""





  UpperCAmelCase__     							=							GPTaTokenizer.from_pretrained('gpt2'			,			pad_token='<|endoftext|>'			,			padding_side='left'					)
  UpperCAmelCase__     							=							tokenizer(['Hello this is a long string', 'Hey']			,			return_tensors='np'			,			padding=UpperCamelCase__			,			truncation=UpperCamelCase__					)

  UpperCAmelCase__     							=							FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'					)
  UpperCAmelCase__     							=							False
  UpperCAmelCase__     							=							model.config.eos_token_id

  UpperCAmelCase__     							=							jax.jit(model.generate					)

  UpperCAmelCase__     							=							jit_generate(
      inputs['input_ids']			,			attention_mask=inputs['attention_mask']			,			pad_token_id=tokenizer.pad_token_id					).sequences

  UpperCAmelCase__     							=							tokenizer.batch_decode(UpperCamelCase__			,			skip_special_tokens=UpperCamelCase__					)

  UpperCAmelCase__     							=							[
      'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
      'Hey, I\'m a little late to the party. I\'m going to',
  ]

  self.assertListEqual(UpperCamelCase__			,			UpperCamelCase__					)




 @is_pt_flax_cross_test
 def     UpperCamelCase__				(self					)					->   str:






  """simple docstring"""





  UpperCAmelCase__							,       UpperCAmelCase__     							=							self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__					):
    # prepare inputs
    UpperCAmelCase__     							=							self._prepare_for_class(UpperCamelCase__			,			UpperCamelCase__					)
    UpperCAmelCase__     							=							{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

    # load corresponding PyTorch class
    UpperCAmelCase__     							=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
    UpperCAmelCase__     							=							getattr(UpperCamelCase__			,			UpperCamelCase__					)

    UpperCAmelCase__							,       UpperCAmelCase__     							=							pt_inputs['input_ids'].shape
    UpperCAmelCase__     							=							np.random.randint(0			,			seq_length - 1			,			size=(batch_size,)					)
    for batch_idx, start_index in enumerate(UpperCamelCase__					):
     UpperCAmelCase__     							=							0
     UpperCAmelCase__     							=							1
     UpperCAmelCase__     							=							0
     UpperCAmelCase__     							=							1
    UpperCAmelCase__     							=							pt_model_class(UpperCamelCase__					).eval()
    UpperCAmelCase__     							=							model_class(UpperCamelCase__			,			dtype=jnp.floataa					)

    UpperCAmelCase__     							=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,			UpperCamelCase__					)
    UpperCAmelCase__     							=							fx_state

    with torch.no_grad():
     UpperCAmelCase__     							=							pt_model(**UpperCamelCase__					).to_tuple()

    UpperCAmelCase__     							=							fx_model(**UpperCamelCase__					).to_tuple()
    self.assertEqual(len(UpperCamelCase__					)			,			len(UpperCamelCase__					)			,			'Output lengths differ between Flax and PyTorch'					)
    for fx_output, pt_output in zip(UpperCamelCase__			,			UpperCamelCase__					):
     self.assert_almost_equals(fx_output[:, -1]			,			pt_output[:, -1].numpy()			,			4E-2					)

    with tempfile.TemporaryDirectory() as tmpdirname:
     pt_model.save_pretrained(UpperCamelCase__					)
     UpperCAmelCase__     							=							model_class.from_pretrained(UpperCamelCase__			,			from_pt=UpperCamelCase__					)

    UpperCAmelCase__     							=							fx_model_loaded(**UpperCamelCase__					).to_tuple()
    self.assertEqual(
        len(UpperCamelCase__					)			,			len(UpperCamelCase__					)			,			'Output lengths differ between Flax and PyTorch'					)
    for fx_output_loaded, pt_output in zip(UpperCamelCase__			,			UpperCamelCase__					):
     self.assert_almost_equals(fx_output_loaded[:, -1]			,			pt_output[:, -1].numpy()			,			4E-2					)




 @is_pt_flax_cross_test
 def     UpperCamelCase__				(self					)					->   str:






  """simple docstring"""





  UpperCAmelCase__							,       UpperCAmelCase__     							=							self.model_tester.prepare_config_and_inputs_for_common()
  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__					):
    # prepare inputs
    UpperCAmelCase__     							=							self._prepare_for_class(UpperCamelCase__			,			UpperCamelCase__					)
    UpperCAmelCase__     							=							{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

    # load corresponding PyTorch class
    UpperCAmelCase__     							=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
    UpperCAmelCase__     							=							getattr(UpperCamelCase__			,			UpperCamelCase__					)

    UpperCAmelCase__     							=							pt_model_class(UpperCamelCase__					).eval()
    UpperCAmelCase__     							=							model_class(UpperCamelCase__			,			dtype=jnp.floataa					)

    UpperCAmelCase__     							=							load_flax_weights_in_pytorch_model(UpperCamelCase__			,			fx_model.params					)
    UpperCAmelCase__							,       UpperCAmelCase__     							=							pt_inputs['input_ids'].shape
    UpperCAmelCase__     							=							np.random.randint(0			,			seq_length - 1			,			size=(batch_size,)					)
    for batch_idx, start_index in enumerate(UpperCamelCase__					):
     UpperCAmelCase__     							=							0
     UpperCAmelCase__     							=							1
     UpperCAmelCase__     							=							0
     UpperCAmelCase__     							=							1

    # make sure weights are tied in PyTorch
    pt_model.tie_weights()

    with torch.no_grad():
     UpperCAmelCase__     							=							pt_model(**UpperCamelCase__					).to_tuple()

    UpperCAmelCase__     							=							fx_model(**UpperCamelCase__					).to_tuple()
    self.assertEqual(len(UpperCamelCase__					)			,			len(UpperCamelCase__					)			,			'Output lengths differ between Flax and PyTorch'					)
    for fx_output, pt_output in zip(UpperCamelCase__			,			UpperCamelCase__					):
     self.assert_almost_equals(fx_output[:, -1]			,			pt_output[:, -1].numpy()			,			4E-2					)

    with tempfile.TemporaryDirectory() as tmpdirname:
     fx_model.save_pretrained(UpperCamelCase__					)
     UpperCAmelCase__     							=							pt_model_class.from_pretrained(UpperCamelCase__			,			from_flax=UpperCamelCase__					)

    with torch.no_grad():
     UpperCAmelCase__     							=							pt_model_loaded(**UpperCamelCase__					).to_tuple()

    self.assertEqual(
        len(UpperCamelCase__					)			,			len(UpperCamelCase__					)			,			'Output lengths differ between Flax and PyTorch'					)
    for fx_output, pt_output in zip(UpperCamelCase__			,			UpperCamelCase__					):
     self.assert_almost_equals(fx_output[:, -1]			,			pt_output[:, -1].numpy()			,			4E-2					)




 @tooslow
 def     UpperCamelCase__				(self					)					->   List[Any]:






  """simple docstring"""





  for model_class_name in self.all_model_classes:
   UpperCAmelCase__     							=							model_class_name.from_pretrained('EleutherAI/gpt-j-6B'					)
   UpperCAmelCase__     							=							model(np.ones((1, 1)					)					)
   self.assertIsNotNone(UpperCamelCase__					)











'''simple docstring'''







# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


__lowerCAmelCase        =							"pytorch_model.bin"
__lowerCAmelCase        =							"pytorch_model.bin.index.json"
__lowerCAmelCase        =							"adapter_config.json"
__lowerCAmelCase        =							"adapter_model.bin"
__lowerCAmelCase        =							"adapter_model.safetensors"
__lowerCAmelCase        =							"tf_model.h5"
__lowerCAmelCase        =							"tf_model.h5.index.json"
__lowerCAmelCase        =							"model.ckpt"
__lowerCAmelCase        =							"flax_model.msgpack"
__lowerCAmelCase        =							"flax_model.msgpack.index.json"
__lowerCAmelCase        =							"model.safetensors"
__lowerCAmelCase        =							"model.safetensors.index.json"
__lowerCAmelCase        =							"config.json"
__lowerCAmelCase        =							"preprocessor_config.json"
__lowerCAmelCase        =							FEATURE_EXTRACTOR_NAME
__lowerCAmelCase        =							"generation_config.json"
__lowerCAmelCase        =							"modelcard.json"

__lowerCAmelCase        =							"▁"
__lowerCAmelCase        =							SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

__lowerCAmelCase        =							[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
__lowerCAmelCase        =							[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
__lowerCAmelCase        =							[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ):
       """simple docstring"""




       if version.parse(lowercase_   ) < version.parse(lowercase_   ):
              if "dev" in min_version:
                     a_						     =			(
                         'This example requires a source install from HuggingFace Transformers (see '
                         '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
                     )
              else:
                     a_						     =			F'This example requires a minimum version of {min_version},'
              error_message += F' but the version found is {__version__}.\n'
              raise ImportError(
                  error_message
                  + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
                  'versions of HuggingFace Transformers.'   )




